Aortic Valve Endothelial Cells and Adhesion Molecules: Implications for a Tissue Engineered Heart Valve

Children with congenital heart defects and patients with faulty or failing valves have the need for a suitable aortic heart valve replacement. Current treatment options have several downfalls and heavy investigation is being done into the design of an engineered valve to find an alternative that would alleviate many of these issues. Understanding the physiology of how cells interact in vivo is crucial to the construction of such valve. This study investigates the effect of cyclic strain in aortic valve endothelial cells on the adhesion molecules, PECAM-1, ƒÒ1-Integrin, VE-Cadherin and Vinculin. Experiments found that cyclic strain plays a role in the development of cell/cell and cell/extracellular matrix adhesions and junctions and is extremely important in the pre-conditioning of a tissue engineered construct. Without this strain the new valve would be more susceptible to inflammation, injury or possible failure after being implanted into the patient

Biometric Person Identification Using Near-infrared Hand-dorsa Vein Images

Biometric recognition is becoming more and more important with the increasing demand for security, and more usable with the improvement of computer vision as well as pattern recognition technologies. Hand vein patterns have been recognised as a good biometric measure for personal identification due to many excellent characteristics, such as uniqueness and stability, as well as difficulty to copy or forge. This thesis covers all the research and development aspects of a biometric person identification system based on near-infrared hand-dorsa vein images. Firstly, the design and realisation of an optimised vein image capture device is presented. In order to maximise the quality of the captured images with relatively low cost, the infrared illumination and imaging theory are discussed. Then a database containing 2040 images from 102 individuals, which were captured by this device, is introduced. Secondly, image analysis and the customised image pre-processing methods are discussed. The consistency of the database images is evaluated using mean squared error (MSE) and peak signal-to-noise ratio (PSNR). Geometrical pre-processing, including shearing correction and region of interest (ROI) extraction, is introduced to improve image consistency. Image noise is evaluated using total variance (TV) values. Grey-level pre-processing, including grey-level normalisation, filtering and adaptive histogram equalisation are applied to enhance vein patterns. Thirdly, a gradient-based image segmentation algorithm is compared with popular algorithms in references like Niblack and Threshold Image algorithm to demonstrate its effectiveness in vein pattern extraction. Post-processing methods including morphological filtering and thinning are also presented. Fourthly, feature extraction and recognition methods are investigated, with several new approaches based on keypoints and local binary patterns (LBP) proposed. Through comprehensive comparison with other approaches based on structure and texture features as well as performance evaluation using the database created with 2040 images, the proposed approach based on multi-scale partition LBP is shown to provide the best recognition performance with an identification rate of nearly 99%. Finally, the whole hand-dorsa vein identification system is presented with a user interface for administration of user information and for person identification